SlideShare a Scribd company logo

Deep-Learning-2017-Lecture5CNN.ppt

•Download as PPT, PDF•
•
A
archn4

srtgdrthrhrhr

Education
1 of 28
Lecture 5 Smaller Network: CNN  We know it is good to learn a small model.  From this fully connected model, do we really need all the edges?  Can some of these be shared?
Consider learning an image: Some patterns are much smaller than the whole image “beak” detector Can represent a small region with fewer parameters
Same pattern appears in different places: They can be compressed! What about training a lot of such “small” detectors and each detector must “move around”. “upper-left beak” detector “middle beak” detector They can be compressed to the same parameters.
A convolutional layer A filter A CNN is a neural network with some convolutional layers (and some other layers). A convolutional layer has a number of filters that does convolutional operation. Beak detector
Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 … … These are the network parameters to be learned. Each filter detects a small pattern (3 x 3).
Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 3 -1 stride=1 Dot product
Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 3 -3 If stride=2
Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 3 -1 -3 -1 -3 1 0 -3 -3 -3 0 1 3 -2 -2 -1 stride=1
Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 3 -1 -3 -1 -3 1 0 -3 -3 -3 0 1 3 -2 -2 -1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 -1 -1 -1 -1 -1 -1 -2 1 -1 -1 -2 1 -1 0 -4 3 Repeat this for each filter stride=1 Two 4 x 4 images Forming 2 x 4 x 4 matrix Feature Map
Color image: RGB 3 channels 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 1 -1 -1 -1 1 -1 -1 -1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 Color image
1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 image convolution -1 1 -1 -1 1 -1 -1 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 1 x 2 x … … 36 x … … 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 Convolution v.s. Fully Connected Fully- connected
1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 1 2 3 … 8 9 … 1 3 14 15 … Only connect to 9 inputs, not fully connected 4: 10: 16 1 0 0 0 0 1 0 0 0 0 1 1 3 fewer parameters!
1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 1: 2: 3: … 7: 8: 9: … 1 3: 14: 15: … 4: 10: 16: 1 0 0 0 0 1 0 0 0 0 1 1 3 -1 Shared weights 6 x 6 image Fewer parameters Even fewer parameters
The whole CNN Fully Connected Feedforward network cat dog …… Convolution Max Pooling Convolution Max Pooling Flattened Can repeat many times
Max Pooling 3 -1 -3 -1 -3 1 0 -3 -3 -3 0 1 3 -2 -2 -1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 -1 -1 -1 -1 -1 -1 -2 1 -1 -1 -2 1 -1 0 -4 3 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1
Why Pooling  Subsampling pixels will not change the object Subsampling bird bird We can subsample the pixels to make image smaller fewer parameters to characterize the image
A CNN compresses a fully connected network in two ways: Reducing number of connections Shared weights on the edges Max pooling further reduces the complexity
Max Pooling 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 3 0 1 3 -1 1 3 0 2 x 2 image Each filter is a channel New image but smaller Conv Max Pooling
The whole CNN Convolution Max Pooling Convolution Max Pooling Can repeat many times A new image The number of channels is the number of filters Smaller than the original image 3 0 1 3 -1 1 3 0
The whole CNN Fully Connected Feedforward network cat dog …… Convolution Max Pooling Convolution Max Pooling Flattened A new image A new image
Flattening 3 0 1 3 -1 1 3 0 Flattened 3 0 1 3 -1 1 0 3 Fully Connected Feedforward network
Only modified the network structure and input format (vector -> 3-D tensor) CNN in Keras Convolution Max Pooling Convolution Max Pooling input 1 -1 -1 -1 1 -1 -1 -1 1 -1 1 -1 -1 1 -1 -1 1 -1 There are 25 3x3 filters. … … Input_shape = ( 28 , 28 , 1) 1: black/white, 3: RGB 28 x 28 pixels 3 -1 -3 1 3
Only modified the network structure and input format (vector -> 3-D array) CNN in Keras Convolution Max Pooling Convolution Max Pooling Input 1 x 28 x 28 25 x 26 x 26 25 x 13 x 13 50 x 11 x 11 50 x 5 x 5 How many parameters for each filter? How many parameters for each filter? 9 225= 25x9
Only modified the network structure and input format (vector -> 3-D array) CNN in Keras Convolution Max Pooling Convolution Max Pooling Input 1 x 28 x 28 25 x 26 x 26 25 x 13 x 13 50 x 11 x 11 50 x 5 x 5 Flattened 1250 Fully connected feedforward network Output
AlphaGo Neural Network (19 x 19 positions) Next move 19 x 19 matrix Black: 1 white: -1 none: 0 Fully-connected feedforward network can be used But CNN performs much better
AlphaGo’s policy network Note: AlphaGo does not use Max Pooling. The following is quotation from their Nature article:
CNN in speech recognition Time Frequency Spectrogram CNN Image The filters move in the frequency direction.
CNN in text classification Source of image: http://citeseerx.ist.psu.edu/viewdoc/downlo ad?doi=10.1.1.703.6858&rep=rep1&type=p df ?

Recommended

convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfSubhamKumar3239
 
Deep-Learning-2017-Lecture5CNN.pptx
Deep-Learning-2017-Lecture5CNN.pptxDeep-Learning-2017-Lecture5CNN.pptx
Deep-Learning-2017-Lecture5CNN.pptxDr. Radhey Shyam
 
Machine learning algorithms like CNN and LSTM
Machine learning algorithms like CNN and LSTMMachine learning algorithms like CNN and LSTM
Machine learning algorithms like CNN and LSTMmonihareni
 
Adv.TopicsAICNN.ppt
Adv.TopicsAICNN.pptAdv.TopicsAICNN.ppt
Adv.TopicsAICNN.pptZeeshanAhmad449947
 
CNN (v2).pptx
CNN (v2).pptxCNN (v2).pptx
CNN (v2).pptxFKKBWITTAINAN
 
convolutional neural networks for machine learning
convolutional neural networks for machine learningconvolutional neural networks for machine learning
convolutional neural networks for machine learningomogire08
 
cnn.pdf
cnn.pdfcnn.pdf
cnn.pdfAKANKSHADIXIT52
 
Practical Deep Learning Using Tensor Flow - Sandeep Kath
Practical Deep Learning Using Tensor Flow - Sandeep KathPractical Deep Learning Using Tensor Flow - Sandeep Kath
Practical Deep Learning Using Tensor Flow - Sandeep KathSandeep Kath
 

Recommended

convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfSubhamKumar3239
 
Deep-Learning-2017-Lecture5CNN.pptx
Deep-Learning-2017-Lecture5CNN.pptxDeep-Learning-2017-Lecture5CNN.pptx
Deep-Learning-2017-Lecture5CNN.pptxDr. Radhey Shyam
 
Machine learning algorithms like CNN and LSTM
Machine learning algorithms like CNN and LSTMMachine learning algorithms like CNN and LSTM
Machine learning algorithms like CNN and LSTMmonihareni
 
Adv.TopicsAICNN.ppt
Adv.TopicsAICNN.pptAdv.TopicsAICNN.ppt
Adv.TopicsAICNN.pptZeeshanAhmad449947
 
CNN (v2).pptx
CNN (v2).pptxCNN (v2).pptx
CNN (v2).pptxFKKBWITTAINAN
 
convolutional neural networks for machine learning
convolutional neural networks for machine learningconvolutional neural networks for machine learning
convolutional neural networks for machine learningomogire08
 
cnn.pdf
cnn.pdfcnn.pdf
cnn.pdfAKANKSHADIXIT52
 
Practical Deep Learning Using Tensor Flow - Sandeep Kath
Practical Deep Learning Using Tensor Flow - Sandeep KathPractical Deep Learning Using Tensor Flow - Sandeep Kath
Practical Deep Learning Using Tensor Flow - Sandeep KathSandeep Kath
 
Deep learning in E-Commerce Applications and Challenges (CNN)
Deep learning in E-Commerce Applications and Challenges (CNN) Deep learning in E-Commerce Applications and Challenges (CNN)
Deep learning in E-Commerce Applications and Challenges (CNN) Houda Bakir
 
CNN
CNNCNN
CNNgeorgejustymirobi1
 
Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
 
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
 
Introduction to computer graphics part 1
Introduction to computer graphics part 1Introduction to computer graphics part 1
Introduction to computer graphics part 1Ankit Garg
 
Unit 1
Unit 1Unit 1
Unit 1Ankit Garg
 
Find nuclei in images with U-net
Find nuclei in images with U-netFind nuclei in images with U-net
Find nuclei in images with U-netDing Li
 
Deep learning simplified
Deep learning simplifiedDeep learning simplified
Deep learning simplifiedLovelyn Rose
 
Display Hardware
Display HardwareDisplay Hardware
Display Hardwareguest56aeb3
 
CNN Basics.pdf
CNN Basics.pdfCNN Basics.pdf
CNN Basics.pdfNiharikaThakur32
 
Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural NetworksAshray Bhandare
 
Deep learning for Computer Vision intro
Deep learning for Computer Vision introDeep learning for Computer Vision intro
Deep learning for Computer Vision introNadav Carmel
 
2. filtering basics
2. filtering basics2. filtering basics
2. filtering basicsAtul Kumar Jha
 
Spatial filtering
Spatial filteringSpatial filtering
Spatial filteringshabanam tamboli
 
Spatial domain filtering.ppt
Spatial domain filtering.pptSpatial domain filtering.ppt
Spatial domain filtering.pptssuser4bbfb1
 
CNN_AH.pptx
CNN_AH.pptxCNN_AH.pptx
CNN_AH.pptxssuserc755f1
 
CNN_AH.pptx
CNN_AH.pptxCNN_AH.pptx
CNN_AH.pptxssuserc755f1
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxEsquimalt MFRC
 
On National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan FellowsOn National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan FellowsMebane Rash
 
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonhttgc7rh9c
 
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17Celine George
 
Simple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfSimple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfstareducators107
 

More Related Content

Similar to Deep-Learning-2017-Lecture5CNN.ppt

Deep learning in E-Commerce Applications and Challenges (CNN)
Deep learning in E-Commerce Applications and Challenges (CNN) Deep learning in E-Commerce Applications and Challenges (CNN)
Deep learning in E-Commerce Applications and Challenges (CNN) Houda Bakir
 
Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
 
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
 
Introduction to computer graphics part 1
Introduction to computer graphics part 1Introduction to computer graphics part 1
Introduction to computer graphics part 1Ankit Garg
 
Find nuclei in images with U-net
Find nuclei in images with U-netFind nuclei in images with U-net
Find nuclei in images with U-netDing Li
 
Deep learning simplified
Deep learning simplifiedDeep learning simplified
Deep learning simplifiedLovelyn Rose
 
Display Hardware
Display HardwareDisplay Hardware
Display Hardwareguest56aeb3
 
Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural NetworksAshray Bhandare
 
Deep learning for Computer Vision intro
Deep learning for Computer Vision introDeep learning for Computer Vision intro
Deep learning for Computer Vision introNadav Carmel
 
Spatial domain filtering.ppt
Spatial domain filtering.pptSpatial domain filtering.ppt
Spatial domain filtering.pptssuser4bbfb1
 

Similar to Deep-Learning-2017-Lecture5CNN.ppt (17)

Deep learning in E-Commerce Applications and Challenges (CNN)
Deep learning in E-Commerce Applications and Challenges (CNN) Deep learning in E-Commerce Applications and Challenges (CNN)
Deep learning in E-Commerce Applications and Challenges (CNN)
 
CNN
CNNCNN
CNN
 
Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural Networks
 
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
 
Introduction to computer graphics part 1
Introduction to computer graphics part 1Introduction to computer graphics part 1
Introduction to computer graphics part 1
 
Unit 1
Unit 1Unit 1
Unit 1
 
Find nuclei in images with U-net
Find nuclei in images with U-netFind nuclei in images with U-net
Find nuclei in images with U-net
 
Deep learning simplified
Deep learning simplifiedDeep learning simplified
Deep learning simplified
 
Display Hardware
Display HardwareDisplay Hardware
Display Hardware
 
CNN Basics.pdf
CNN Basics.pdfCNN Basics.pdf
CNN Basics.pdf
 
Convolutional Neural Networks
Convolutional Neural NetworksConvolutional Neural Networks
Convolutional Neural Networks
 
Deep learning for Computer Vision intro
Deep learning for Computer Vision introDeep learning for Computer Vision intro
Deep learning for Computer Vision intro
 
2. filtering basics
2. filtering basics2. filtering basics
2. filtering basics
 
Spatial filtering
Spatial filteringSpatial filtering
Spatial filtering
 
Spatial domain filtering.ppt
Spatial domain filtering.pptSpatial domain filtering.ppt
Spatial domain filtering.ppt
 
CNN_AH.pptx
CNN_AH.pptxCNN_AH.pptx
CNN_AH.pptx
 
CNN_AH.pptx
CNN_AH.pptxCNN_AH.pptx
CNN_AH.pptx
 

Recently uploaded

HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxEsquimalt MFRC
 
On National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan FellowsOn National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan FellowsMebane Rash
 
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonhttgc7rh9c
 
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17Celine George
 
Simple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfSimple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfstareducators107
 
Wellbeing inclusion and digital dystopias.pptx
Wellbeing inclusion and digital dystopias.pptxWellbeing inclusion and digital dystopias.pptx
Wellbeing inclusion and digital dystopias.pptxJisc
 
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdfUGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdfNirmal Dwivedi
 
REMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptxREMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptxDr. Ravikiran H M Gowda
 
Towards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptxTowards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptxJisc
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024Elizabeth Walsh
 
Play hard learn harder: The Serious Business of Play
Play hard learn harder: The Serious Business of PlayPlay hard learn harder: The Serious Business of Play
Play hard learn harder: The Serious Business of PlayPooky Knightsmith
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Jisc
 
Tatlong Kwento ni Lola basyang-1.pdf arts
Tatlong Kwento ni Lola basyang-1.pdf artsTatlong Kwento ni Lola basyang-1.pdf arts
Tatlong Kwento ni Lola basyang-1.pdf artsNbelano25
 
Python Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxPython Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxRamakrishna Reddy Bijjam
 
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxInterdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxPooja Bhuva
 
OSCM Unit 2_Operations Processes & Systems
OSCM Unit 2_Operations Processes & SystemsOSCM Unit 2_Operations Processes & Systems
OSCM Unit 2_Operations Processes & SystemsSandeep D Chaudhary
 
How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17Celine George
 
Understanding Accommodations and Modifications
Understanding Accommodations and ModificationsUnderstanding Accommodations and Modifications
Understanding Accommodations and ModificationsMJDuyan
 
Economic Importance Of Fungi In Food Additives
Economic Importance Of Fungi In Food AdditivesEconomic Importance Of Fungi In Food Additives
Economic Importance Of Fungi In Food AdditivesSHIVANANDaRV
 

Recently uploaded (20)

HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
On National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan FellowsOn National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan Fellows
 
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
 
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17
 
Simple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfSimple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdf
 
Wellbeing inclusion and digital dystopias.pptx
Wellbeing inclusion and digital dystopias.pptxWellbeing inclusion and digital dystopias.pptx
Wellbeing inclusion and digital dystopias.pptx
 
OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...
 
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdfUGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
 
REMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptxREMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptx
 
Towards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptxTowards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptx
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024
 
Play hard learn harder: The Serious Business of Play
Play hard learn harder: The Serious Business of PlayPlay hard learn harder: The Serious Business of Play
Play hard learn harder: The Serious Business of Play
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
Tatlong Kwento ni Lola basyang-1.pdf arts
Tatlong Kwento ni Lola basyang-1.pdf artsTatlong Kwento ni Lola basyang-1.pdf arts
Tatlong Kwento ni Lola basyang-1.pdf arts
 
Python Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxPython Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docx
 
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxInterdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptx
 
OSCM Unit 2_Operations Processes & Systems
OSCM Unit 2_Operations Processes & SystemsOSCM Unit 2_Operations Processes & Systems
OSCM Unit 2_Operations Processes & Systems
 
How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17
 
Understanding Accommodations and Modifications
Understanding Accommodations and ModificationsUnderstanding Accommodations and Modifications
Understanding Accommodations and Modifications
 
Economic Importance Of Fungi In Food Additives
Economic Importance Of Fungi In Food AdditivesEconomic Importance Of Fungi In Food Additives
Economic Importance Of Fungi In Food Additives
 

Deep-Learning-2017-Lecture5CNN.ppt

  • 1. Lecture 5 Smaller Network: CNN  We know it is good to learn a small model.  From this fully connected model, do we really need all the edges?  Can some of these be shared?
  • 2. Consider learning an image: Some patterns are much smaller than the whole image “beak” detector Can represent a small region with fewer parameters
  • 3. Same pattern appears in different places: They can be compressed! What about training a lot of such “small” detectors and each detector must “move around”. “upper-left beak” detector “middle beak” detector They can be compressed to the same parameters.
  • 4. A convolutional layer A filter A CNN is a neural network with some convolutional layers (and some other layers). A convolutional layer has a number of filters that does convolutional operation. Beak detector
  • 5. Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 … … These are the network parameters to be learned. Each filter detects a small pattern (3 x 3).
  • 6. Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 3 -1 stride=1 Dot product
  • 7. Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 3 -3 If stride=2
  • 8. Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 3 -1 -3 -1 -3 1 0 -3 -3 -3 0 1 3 -2 -2 -1 stride=1
  • 9. Convolution 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 3 -1 -3 -1 -3 1 0 -3 -3 -3 0 1 3 -2 -2 -1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 -1 -1 -1 -1 -1 -1 -2 1 -1 -1 -2 1 -1 0 -4 3 Repeat this for each filter stride=1 Two 4 x 4 images Forming 2 x 4 x 4 matrix Feature Map
  • 10. Color image: RGB 3 channels 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 1 -1 -1 -1 1 -1 -1 -1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 Color image
  • 11. 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 image convolution -1 1 -1 -1 1 -1 -1 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 1 x 2 x … … 36 x … … 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 Convolution v.s. Fully Connected Fully- connected
  • 12. 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 1 2 3 … 8 9 … 1 3 14 15 … Only connect to 9 inputs, not fully connected 4: 10: 16 1 0 0 0 0 1 0 0 0 0 1 1 3 fewer parameters!
  • 13. 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1 1: 2: 3: … 7: 8: 9: … 1 3: 14: 15: … 4: 10: 16: 1 0 0 0 0 1 0 0 0 0 1 1 3 -1 Shared weights 6 x 6 image Fewer parameters Even fewer parameters
  • 14. The whole CNN Fully Connected Feedforward network cat dog …… Convolution Max Pooling Convolution Max Pooling Flattened Can repeat many times
  • 15. Max Pooling 3 -1 -3 -1 -3 1 0 -3 -3 -3 0 1 3 -2 -2 -1 -1 1 -1 -1 1 -1 -1 1 -1 Filter 2 -1 -1 -1 -1 -1 -1 -2 1 -1 -1 -2 1 -1 0 -4 3 1 -1 -1 -1 1 -1 -1 -1 1 Filter 1
  • 16. Why Pooling  Subsampling pixels will not change the object Subsampling bird bird We can subsample the pixels to make image smaller fewer parameters to characterize the image
  • 17. A CNN compresses a fully connected network in two ways: Reducing number of connections Shared weights on the edges Max pooling further reduces the complexity
  • 18. Max Pooling 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 6 x 6 image 3 0 1 3 -1 1 3 0 2 x 2 image Each filter is a channel New image but smaller Conv Max Pooling
  • 19. The whole CNN Convolution Max Pooling Convolution Max Pooling Can repeat many times A new image The number of channels is the number of filters Smaller than the original image 3 0 1 3 -1 1 3 0
  • 20. The whole CNN Fully Connected Feedforward network cat dog …… Convolution Max Pooling Convolution Max Pooling Flattened A new image A new image
  • 21. Flattening 3 0 1 3 -1 1 3 0 Flattened 3 0 1 3 -1 1 0 3 Fully Connected Feedforward network
  • 22. Only modified the network structure and input format (vector -> 3-D tensor) CNN in Keras Convolution Max Pooling Convolution Max Pooling input 1 -1 -1 -1 1 -1 -1 -1 1 -1 1 -1 -1 1 -1 -1 1 -1 There are 25 3x3 filters. … … Input_shape = ( 28 , 28 , 1) 1: black/white, 3: RGB 28 x 28 pixels 3 -1 -3 1 3
  • 23. Only modified the network structure and input format (vector -> 3-D array) CNN in Keras Convolution Max Pooling Convolution Max Pooling Input 1 x 28 x 28 25 x 26 x 26 25 x 13 x 13 50 x 11 x 11 50 x 5 x 5 How many parameters for each filter? How many parameters for each filter? 9 225= 25x9
  • 24. Only modified the network structure and input format (vector -> 3-D array) CNN in Keras Convolution Max Pooling Convolution Max Pooling Input 1 x 28 x 28 25 x 26 x 26 25 x 13 x 13 50 x 11 x 11 50 x 5 x 5 Flattened 1250 Fully connected feedforward network Output
  • 25. AlphaGo Neural Network (19 x 19 positions) Next move 19 x 19 matrix Black: 1 white: -1 none: 0 Fully-connected feedforward network can be used But CNN performs much better
  • 26. AlphaGo’s policy network Note: AlphaGo does not use Max Pooling. The following is quotation from their Nature article:
  • 27. CNN in speech recognition Time Frequency Spectrogram CNN Image The filters move in the frequency direction.
  • 28. CNN in text classification Source of image: http://citeseerx.ist.psu.edu/viewdoc/downlo ad?doi=10.1.1.703.6858&rep=rep1&type=p df ?